State-Augmented Graphs for Circular Economy Triage
- URL: http://arxiv.org/abs/2512.15824v1
- Date: Wed, 17 Dec 2025 16:23:47 GMT
- Title: State-Augmented Graphs for Circular Economy Triage
- Abstract summary: Circular economy (CE) triage is the assessment of products to determine which sustainable pathway they can follow once they reach the end of their usefulness.<n>This paper presents a novel decision-making framework as a simple deterministic solver over a state-augmented Disassembly Sequencing Planning graph.
- Score: 2.7459577246953963
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Circular economy (CE) triage is the assessment of products to determine which sustainable pathway they can follow once they reach the end of their usefulness as they are currently being used. Effective CE triage requires adaptive decisions that balance retained value against the costs and constraints of processing and labour. This paper presents a novel decision-making framework as a simple deterministic solver over a state-augmented Disassembly Sequencing Planning (DSP) graph. By encoding the disassembly history into the state, our framework enforces the Markov property, enabling optimal, recursive evaluation by ensuring each decision only depends on the previous state. The triage decision involves choices between continuing disassembly or committing to a CE option. The model integrates condition-aware utility based on diagnostic health scores and complex operational constraints. We demonstrate the framework's flexibility with a worked example: the hierarchical triage of electric vehicle (EV) batteries, where decisions are driven by the recursive valuation of components. The example illustrates how a unified formalism enables the accommodation of varying mechanical complexity, safety requirements, and economic drivers. This unified formalism therefore provides a tractable and generalisable foundation for optimising CE triage decisions across diverse products and operational contexts.
Related papers
- Revelation Control [0.0]
Revelation Control is the problem of choosing priced interventions that reveal hidden state only insofar as the revealed distinctions can change a consequential decision.<n>We develop this theory for learning systems, where states equivalent under declared current information can respond differently to future training and favor different actions.
arXiv Detail & Related papers (2026-08-24T22:07:57Z) - Decision-Value Attribution in Predict-then-Optimize Systems [42.371764229953165]
Decision Value Attribution (DVA) is a framework for attributing the value of a fixed prediction--optimization pipeline.<n>We present three variants: InfoDVA attributes value to features, DesignDVA attributes value to operational configurations, and Decision-Value Interactions (DVI) quantifies how information and design jointly create value.
arXiv Detail & Related papers (2026-06-29T07:17:05Z) - Causal Algorithmic Recourse: Foundations and Methods [57.73269033551628]
Key feature of AI systems is the ability to provide recommendations for how an individual may reverse a negative decision.<n>Existing approaches treat recourse outcomes as counterfactuals of a fixed unit.<n>We develop a causal framework that models recourse as a process over pre- and post-intervention outcomes.
arXiv Detail & Related papers (2026-05-12T00:55:19Z) - Four-Axis Decision Alignment for Long-Horizon Enterprise AI Agents [0.0]
Long-horizon enterprise agents make high-stakes decisions under lossy memory, multi-step reasoning, and binding regulatory constraints.<n>We propose that long-horizon decision behavior decomposes into four axes, each independently measurable and failable.
arXiv Detail & Related papers (2026-04-21T13:37:19Z) - Event-Centric World Modeling with Memory-Augmented Retrieval for Embodied Decision-Making [0.0]
We propose an event-centric world modeling framework with memory-augmented retrieval for embodied decision-making.<n>The framework represents the environment as a structured set of semantic events, which are encoded into a permutation-invariant latent representation.<n>Decision-making is performed via retrieval over a knowledge bank of prior experiences, where each entry associates an event representation with a corresponding maneuver.
arXiv Detail & Related papers (2026-04-08T06:14:46Z) - EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies [61.267836443398124]
We introduce EcoGym, a benchmark for continuous plan-and-execute decision making in interactive economies.<n>EcoGym is released as an open, testbed for transparent long-horizonity agent evaluation and for studying controllability-utility trade-offs in realistic economic settings.
arXiv Detail & Related papers (2026-02-10T08:12:23Z) - Case-Guided Sequential Assay Planning in Drug Discovery [2.8529443025686487]
Implicit Bayesian Markov Decision Process (IBMDP) is a model-based RL framework designed for simulator-free settings.<n>IBMDP generates stable policies that balance information gain toward desired outcomes with resource efficiency.<n>On a real-world central nervous system (CNS) drug discovery task, IBMDP reduced resource consumption by up to 92% compared to establisheds.
arXiv Detail & Related papers (2026-01-21T06:58:01Z) - CSCBench: A PVC Diagnostic Benchmark for Commodity Supply Chain Reasoning [9.840374380176135]
Large Language Models (LLMs) have achieved remarkable success in general benchmarks, yet their competence in commodity supply chains remains under-explored.<n>We introduce CSCBench, a 2.3K+ single-choice benchmark for CSC reasoning, instantiated through our PVC 3D Evaluation Framework.
arXiv Detail & Related papers (2026-01-05T06:44:29Z) - DecisionFlow: Advancing Large Language Model as Principled Decision Maker [49.088778182807395]
DecisionFlow is a novel decision modeling framework that guides models to reason over structured representations of actions, attributes, and constraints.<n>Rather than predicting answers directly from prompts, DecisionFlow builds a semantically grounded decision space and infers a latent utility function.<n> Empirical results show that DecisionFlow achieves up to 30% accuracy gains over strong prompting baselines.
arXiv Detail & Related papers (2025-05-27T16:23:53Z) - An Identifiable Cost-Aware Causal Decision-Making Framework Using Counterfactual Reasoning [18.324601057882386]
We propose a minimum-cost causal decision (MiCCD) framework via counterfactual reasoning to solve the necessary cause.<n> Emphasis is placed on making counterfactual reasoning processes identifiable in the presence of mixed anomaly data.<n>MiCCD outperforms conventional methods across multiple metrics, including F1-score, cost efficiency, and ranking quality(nDCG@k values)
arXiv Detail & Related papers (2025-05-13T08:41:45Z) - Robust Confinement State Classification with Uncertainty Quantification through Ensembled Data-Driven Methods [39.27649013012046]
We develop methods for confinement state classification with uncertainty quantification and model robustness.<n>We focus on off-line analysis for TCV discharges, distinguishing L-mode, H-mode, and an in-between dithering phase (D)<n>A dataset of 302 TCV discharges is fully labeled, and will be publicly released.
arXiv Detail & Related papers (2025-02-24T18:25:22Z) - Coarse Set Theory for AI Ethics and Decision-Making: A Mathematical Framework for Granular Evaluations [0.0]
Coarse Ethics (CE) is a theoretical framework that justifies coarse-grained evaluations, such as letter grades or warning labels, as ethically appropriate under cognitive and contextual constraints.<n>This paper introduces Coarse Set Theory (CST), a novel mathematical framework that models coarse-grained decision-making using totally ordered structures and coarse partitions.<n>CST defines hierarchical relations among sets and uses information-theoretic tools, such as Kullback-Leibler Divergence, to quantify the trade-off between simplification and information loss.
arXiv Detail & Related papers (2025-02-11T08:18:37Z) - Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge [78.28188747489769]
We propose EvalPlanner, a preference optimization algorithm for Thinking-LLM-as-a-Judge.<n>In a self-training loop, EvalPlanner iteratively optimize over synthetically constructed evaluation plans and executions.<n>Our method achieves a new state-of-the-art performance for generative reward models on RewardBench.
arXiv Detail & Related papers (2025-01-30T02:21:59Z) - Accountability in Offline Reinforcement Learning: Explaining Decisions
with a Corpus of Examples [70.84093873437425]
This paper introduces the Accountable Offline Controller (AOC) that employs the offline dataset as the Decision Corpus.
AOC operates effectively in low-data scenarios, can be extended to the strictly offline imitation setting, and displays qualities of both conservation and adaptability.
We assess AOC's performance in both simulated and real-world healthcare scenarios, emphasizing its capability to manage offline control tasks with high levels of performance while maintaining accountability.
arXiv Detail & Related papers (2023-10-11T17:20:32Z) - Rational Decision-Making Agent with Internalized Utility Judgment [88.01612847081677]
Large language models (LLMs) have demonstrated remarkable advancements and have attracted significant efforts to develop LLMs into agents capable of executing intricate multi-step decision-making tasks beyond traditional NLP applications.<n>This paper proposes RadAgent, which fosters the development of its rationality through an iterative framework involving Experience Exploration and Utility Learning.<n> Experimental results on the ToolBench dataset demonstrate RadAgent's superiority over baselines, achieving over 10% improvement in Pass Rate on diverse tasks.
arXiv Detail & Related papers (2023-08-24T03:11:45Z) - Stein Variational Model Predictive Control [130.60527864489168]
Decision making under uncertainty is critical to real-world, autonomous systems.
Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex distributions.
We show that this framework leads to successful planning in challenging, non optimal control problems.
arXiv Detail & Related papers (2020-11-15T22:36:59Z) - Structural Estimation of Partially Observable Markov Decision Processes [3.1614382994158956]
We consider the structural estimation of the primitives of a POMDP model based upon the observable history of the process.
We illustrate the estimation methodology with an application to optimal equipment replacement.
arXiv Detail & Related papers (2020-08-02T15:04:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.